Delay Discounting and Family History of Psychopathology in Children Ages 9-11: Results from the ABCD Study
Bibliographic record
Abstract
ABSTRACT Delay discounting is a tendency to devalue delayed rewards compared to immediate rewards. Evidence suggests that steeper delay discounting is associated with psychiatric disorders across diagnostic categories, but it is unclear whether steeper delay discounting is a risk factor for these disorders. We examined whether children at higher risk for psychiatric disorders, based on family history, would demonstrate steeper delay discounting behavior. We examined the relationship between delay discounting behavior and family history of psychopathology using data from the Adolescent Brain Cognitive Development (ABCD) study, a nationally representative sample of 11,878 children. Participants completed the delay discounting task between the ages of 9 and 11. We computed Spearman’s correlations between family pattern density of psychiatric disorders and delay discounting behavior. We conducted mixed effects models to examine associations between family pattern density of psychiatric disorders and delay discounting while accounting for sociodemographic factors. Correlations between family history of psychopathology and delay discounting behavior were small, ranging from ρ = –0.02 to 0.04. In mixed effects models, family history of psychopathology was not associated with steeper delay discounting behavior. Sociodemographic factors played a larger role in predicting delay discounting behavior than family history of psychopathology. Race, ethnicity, sex, parental education, and marital status were all significantly associated with delay discounting behavior. These results do not support the hypothesis that children with greater risk for psychopathology display steeper delay discounting behavior. Sociodemographic factors play a larger role in determining delay discounting behavior in this age group.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".